Overview

Amazon SQS

Stacks2.8K

Followers2.0K

Votes171

StreamSets

Stacks53

Followers133

Votes0

Amazon SQS vs StreamSets: What are the differences?

Introduction:

Both Amazon SQS (Simple Queue Service) and StreamSets are tools that help in building scalable and resilient data processing workflows. However, they have key differences that make them suitable for different use cases.

Type of Service: Amazon SQS is a message queuing service that allows decoupling of application components, whereas StreamSets is a data integration platform that focuses on moving data between different sources and destinations seamlessly.
Data Processing: Amazon SQS primarily focuses on message-oriented communication, where messages are stored in a queue and processed by consumers, whereas StreamSets deals with transforming, processing, and moving large volumes of data in real-time from various sources to targets or destinations.
Flexibility: Amazon SQS provides a highly scalable and reliable messaging system, suitable for asynchronous communication between microservices or components, while StreamSets offers a flexible data pipeline design tool that enables users to define complex data processing workflows easily.
Integration Capabilities: StreamSets offers extensive connectivity to various data sources and destinations such as databases, cloud services, IoT devices, and more, allowing users to ingest and process data from diverse sources efficiently, whereas Amazon SQS is more focused on message delivery and does not have the same breadth of integration options.
Real-time Processing: StreamSets excels in real-time data processing scenarios where data needs to be processed and delivered instantly, making it ideal for streaming data applications, while Amazon SQS is better suited for handling asynchronous processing tasks where strict real-time requirements are not critical.
Cost Structure: Amazon SQS charges based on the number of requests and data transferred, making it cost-effective for applications with varying workloads, while StreamSets follows a subscription-based pricing model, which may be more suitable for organizations with predictable data processing needs.

In Summary, Amazon SQS is more focused on message queuing and asynchronous communication, while StreamSets is geared towards real-time data processing and integration workflows.

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Advice on Amazon SQS, StreamSets

MITHIRIDI

Software Engineer at LightMetrics

May 8, 2020

Needs adviceon

Amazon SQS

Amazon MQ

I want to schedule a message. Amazon SQS provides a delay of 15 minutes, but I want it in some hours.

Example: Let's say a Message1 is consumed by a consumer A but somehow it failed inside the consumer. I would want to put it in a queue and retry after 4hrs. Can I do this in Amazon MQ? I have seen in some Amazon MQ videos saying scheduling messages can be done. But, I'm not sure how.

303k views303k

Comments

Detailed Comparison

Amazon SQS	StreamSets
Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.	An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
A queue can be created in any region.;The message payload can contain up to 256KB of text in any format. Each 64KB ‘chunk’ of payload is billed as 1 request. For example, a single API call with a 256KB payload will be billed as four requests.;Messages can be sent, received or deleted in batches of up to 10 messages or 256KB. Batches cost the same amount as single messages, meaning SQS can be even more cost effective for customers that use batching.;Long polling reduces extraneous polling to help you minimize cost while receiving new messages as quickly as possible. When your queue is empty, long-poll requests wait up to 20 seconds for the next message to arrive. Long poll requests cost the same amount as regular requests.;Messages can be retained in queues for up to 14 days.;Messages can be sent and read simultaneously.;Developers can get started with Amazon SQS by using only five APIs: CreateQueue, SendMessage, ReceiveMessage, ChangeMessageVisibility, and DeleteMessage. Additional APIs are available to provide advanced functionality.	Only StreamSets provides a single design experience for all design patterns (batch, streaming, CDC, ETL, ELT, and ML pipelines) for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps.
Statistics
Stacks 2.8K	Stacks 53
Followers 2.0K	Followers 133
Votes 171	Votes 0
Pros & Cons
Pros 62 Easy to use, reliable 40 Low cost 28 Simple 14 Doesn't need to maintain it 8 It is Serverless Cons 2 Has a max message size (currently 256K) 2 Difficult to configure 2 Proprietary 1 Has a maximum 15 minutes of delayed messages only	Cons 2 No user community 1 Crashes
Integrations
No integrations available	HBase Databricks Amazon Redshift MySQL gRPC Google BigQuery Amazon Kinesis Cassandra Hadoop Redis

What are some alternatives to Amazon SQS, StreamSets?

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.